Yuan-Jie Chen | Searcharxiv

arXiv2025

Hydrodynamic Whispering: Enabling Near-Field Silent Communication via Artificial Lateral Line Arrays

To address the imperative for covert underwater swarm coordination, this paper introduces "Hydrodynamic Whispering," a near-field silent communication paradigm utilizing Artificial Lateral Line (ALL) arrays. Grounded in potential flow theory, we model the transmitter as an oscillating dipole source. The resulting pressure field exhibits steep nearfield attenuation (scaling with 1/r^2, naturally delimiting a secure "communication bubble" with intrinsic Low Probability of Interception (LPI) properties. We propose a transceiver architecture featuring a Binary Phase Shift Keying (BPSK) modulation scheme adapted for mechanical actuator inertia, coupled with a bio-inspired 24-sensor conformal array. To mitigate low Signal-to-Noise Ratio (SNR) in turbulent environments,a Spatio-Temporal Joint Processing framework incorporating Spatial Matched-Field Beamforming is developed. Simulation results demonstrate that the system achieves an array gain of approximately 13.8 dB and maintains a near-zero Bit Error Rate (BER) within the effective range. This study validates the feasibility of utilizing localized hydrodynamic pressure fluctuations for reliable and secure short-range underwater networking.

eess.SP↗

arXiv2025

Computational fluid dynamics-based structure optimization of ultra-high-pressure water-jet nozzle using approximation method

Since the geometry structure of ultra-high-pressure (UHP) water-jet nozzle is a critical factor to enhance its hydrodynamic performance, it is critical to obtain a suitable geometry for a UHP water jet nozzle. In this study, a CFD-based optimization loop for UHP nozzle structure has been developed by integrating an approximate model to optimize nozzle structure for increasing the radial peak wall shear stress. In order to improve the optimization accuracy of the sparrow search algorithm (SSA), an enhanced version called the Logistic-Tent chaotic sparrow search algorithm (LTC-SSA) is proposed. The LTC-SSA algorithm utilizes the Logistic-Tent Chaotic (LTC) map, which is designed by combining the Logistic and Tent maps. This new approach aims to overcome the shortcoming of "premature convergence" for the SSA algorithm by increasing the diversity of the sparrow population. In addition, to improve the prediction accuracy of peak wall shear stress, a data prediction method based on LTC-SSA-support vector machine (SVM) is proposed. Herein, LTC-SSA algorithm is used to train the penalty coefficient C and parameter gamma g of SVM model. In order to build LTC-SSA-SVM model, optimal Latin hypercube design (Opt LHD) is used to design the sampling nozzle structures, and the peak wall shear stress (objective function) of these nozzle structures are calculated by CFD method. For the purpose of this article, this optimization framework has been employed to optimize original nozzle structure. The results show that the optimization framework developed in this study can be used to optimize nozzle structure with significantly improved its hydrodynamic performance.

physics.flu-dyn↗